dataset docs

docs/src/training/training.md

@@ -32,6 +32,32 @@ Flux.train!(loss, data, opt)
 
 The loss will almost always be defined in terms of some *cost function* that measures the distance of the prediction `m(x)` from the target `y`. Flux has several of these built in, like `mse` for mean squared error or `logloss` for cross entropy loss, but you can calculate it however you want.
 
+## Datasets
+
+The `data` argument provides a collection of data to train with (usually a set of inputs `x` and a target outputs `y`). For example, here's a dummy data set with only one data point:
+
+```julia
+x = rand(784)
+y = rand(10)
+data = [(x, y)]
+```
+
+`Flux.train!` will call `loss(x, y)`, calculate gradients, update the weights and then move on to the next data point if there is one. We can train the model on the same data three times:
+
+```julia
+data = [(x, y), (x, y), (x, y)]
+# Or equivalently
+data = Iterators.repeated((x, y), 3)
+```
+
+It's common to load the `x`s and `y`s separately. In this case you can use `zip`:
+
+```julia
+xs = [rand(784), rand(784), rand(784)]
+ys = [rand( 10), rand( 10), rand( 10)]
+data = zip(xs, ys)
+```
+
 ## Callbacks
 
 `train!` takes an additional argument, `cb`, that's used for callbacks so that you can observe the training process. For example: